Richard C. J. Somerville

Scripps Institution of Oceanography
University of California, San Diego
La Jolla, CA

New observations and theoretical developments have recently led to significant advances in parameterizations of cloud processes for both climate and numerical weather prediction models. When a single-column model (SCM), which consists of one isolated column of a global atmospheric model, is forced with sufficiently accurate observational estimates of horizontal advection terms, the parameterizations within the SCM produce time-dependent fields which can be compared directly with measurements. Within the limitations of SCMs, this comparison provides a straightforward evaluation of the realism of the parameterizations. In the case of cloud microphysical schemes, the fields available from the SCM include cloud altitude, cloud amount, liquid and ice content, particle size spectra, and radiative fluxes at both the surface and the top of the atmosphere. Comparisons of these SCM products with data from the Atmospheric Radiation Measurement (ARM) Program show conclusively that prognostic cloud algorithms with detailed microphysics can be made far more realistic than simpler diagnostic approaches. Using ARM measurements, long-term comparisons of quantities strongly modulated by clouds, such as downwelling surface shortwave radiation, clearly demonstrate the potential superiority of parameterizations based on comprehensive treatments of cloud microphysics and cloud-radiative interactions. A necessary next step is to test these new parameterization concepts thoroughly in state-of-the-art operational numerical weather prediction models and climate models.